Trajectory Simplification: On Minimizing the Direction-based Error

Trajectory data is central to many applications with moving objects. Raw trajectory data is usually very large, and so is simplified before it is stored and processed. Many trajectory simplification notions have been proposed, and among them, the direction-preserving trajectory simplification (DPTS) which aims at protecting the direction information has been shown to perform quite well. However, existing studies on DPTS require users to specify an error tolerance which users might not know how to set properly in some cases (e.g., the error tolerance could only be known at some future time and simply setting one error tolerance does not meet the needs since the simplified trajectories would usually be used in many different applications which accept different error tolerances). In these cases, a better solution is to minimize the error while achieving a pre-defined simplification size. For this purpose, in this paper, we define a problem called Min-Error and develop two exact algorithms and one 2-factor approximate algorithm for the problem. Extensive experiments on real datasets verified our algorithms.